ARAIM-related fault subset optimization algorithm based on sparrow search algorithm
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摘要:
针对多假设解分离(MHSS)测试受卫星数目增加、潜在故障概率提高的影响，使得需要监测的子集数量增长而带来计算负担增加的问题，提出一种基于麻雀搜索算法(SSA)的高级接收机自主完好性监测(ARAIM)故障子集优化算法。结合SSA将可见卫星分配为发现者、跟随者和侦查预警者，通过剔除能量较低的个体降低计算冗余。在搜索过程中，引入自适应步长提升迭代速度，提高算法的执行效率。在双星座情况下，对完好性支持信息(ISM)参数进行3种假设，验证了所提算法的可用性，并与传统算法进行了对比分析。结果表明：通过所提算法得到的子集数量较传统算法减少了75%～90%，相同条件下，仿真用时降低了68%～88%，ARAIM可用性变化不超过2%。
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关键词:
- 高级接收机自主完好性监测 /
- 双星座 /
- 故障子集 /
- 麻雀搜索算法 /
- 完好性支持信息
Abstract:The multiple hypothesis solution separation (MHSS) test is affected by the increase in the number of satellites and the potential fault probability. This results in a sharp increase in the number of subsets to be monitored and brings more computational burdens. In order to solve the above problems, an advanced receiver autonomous integrity monitoring (ARAIM)-related fault subset optimization algorithm based on the sparrow search algorithm (SSA) was proposed. According to the SSA, the satellites were divided into detectors, followers, and premonitors. Computational redundancy was reduced by eliminating individuals with lower energy. The adaptive step size was introduced during the search, so as to improve the iteration speed and the execution efficiency of the algorithm. In the dual constellation scenario, three hypotheses were made for the integrity supported message (ISM) parameters to verify the availability of the improved algorithm and compare it with traditional algorithms. The results show that the number of subsets obtained by the proposed algorithm is reduced by 75%–90% compared with traditional algorithms, and the computing time under the same condition is reduced by 68%–88%. In addition, the availability of ARAIM changes by no more than 2%.
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图 2 第1组参数下ARAIM全球可用性对比
Figure 2. Comparison of global availability of ARAIM with parameters of group 1
图 3 第2组参数下ARAIM全球可用性对比
Figure 3. Comparison of global availability of ARAIM with parameters of group 2
图 4 第3组参数下ARAIM全球可用性对比
Figure 4. Comparison of global availability of ARAIM with parameters of group 3
图 5 第4组参数下ARAIM全球可用性对比
Figure 5. Comparison of global availability of ARAIM with parameters of group 4
图 6 第5组参数下ARAIM全球可用性对比
Figure 6. Comparison of global availability of ARAIM with parameters of group 5
图 7 第6组参数下ARAIM全球可用性对比
Figure 7. Comparison of global availability of ARAIM with parameters of group 6
表 1 ISM参数设置
Table 1. Setting of ISM parameters
假设状态 组号 星座 $ {P_{{\mathrm{sat}}}} $ $ {P_{{\mathrm{const}}}} $ $ {b_{{\mathrm{nom}}}} $ $ {\sigma _{{\mathrm{URA}}}} $ $ {\sigma _{{\mathrm{URE}}}} $ 理想状态 1 GPS 10−5 10−8 0.75 1 0.66 BDS 10−4 10−8 0.75 1 0.66 2 GPS 10−4 10−8 0.75 1 0.66 BDS 10−5 10−8 0.75 1 0.66 差异状态 3 GPS 10−5 10−4 0.75 1 0.66 BDS 10−4 10−5 0.75 1 0.66 4 GPS 10−4 10−5 0.75 1 0.66 BDS 10−5 10−4 0.75 1 0.66 保守状态 5 GPS 10−4 10−4 0.75 2.4 1.6 BDS 10−3 10−4 0.75 2.4 1.6 6 GPS 10−3 10−4 0.75 2.4 1.6 BDS 10−4 10−4 0.75 2.4 1.6 
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表 2 不同ISM参数下$ {N_{\max }} $取值
Table 2. $ {N_{\max }} $ under different ISM parameters
Pevent Nmax N=10 N=15 N=20 N=20 N=30 N=35 N=40 10−5 1 1 1 1 1 1 1 10−4 2 2 2 2 2 2 2 5×10−4 2 2 3 3 3 3 3 10−3 3 3 3 3 3 3 4
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